Bibliographic record
Abstract
The Great Lakes Forest region covers 20 Million Hectares or 20% of Ontario and has 62% forest cover (MNRF, 2018). The property selected for this study is in the northwestern section of Lanark County, where the forest cover is 75% with many stands 80 to 100 years old (Sentesy, 2008). The logging of this resource played a pivotal role in the early economic development of the province. Trees were not only cut for timber, but large tracts of stands were burnt to clear land for farming (Ken A. Elliott, 1998). Later the advent of industrial age increased demand for charcoal and severe cutting of the forest occurred in 1940’s (Ken A. Elliott, 1998). To achieve the objective of restoring the old growth characteristics, it was decided that accurate data of the present tree species composition and age class distribution would be helpful. To accomplish this objective a sub set of the Vegetation Sampling Protocol (VSP) (Danijela Puric-Mladenovic, 2018), was used to collect data in the summer of 2018. Comparing this data with old land surveys, Historical Records (Sankey, 2010) and the Forest Resource Inventory (FRI) provided by Ministry of Natural Resources and Forestry (MNRF) reveals that Acer saccharinum Linnaeus (silver maple), Pinus resinosa Aiton (red pine), Quercus alba (white oak), Larix laricina (Du Roi) K. Koch (tamarack), Picea rubens Sargent (red spruce), were once present on site but not found now. Very few Tsuga (Endler) Carrière (hemlock) (0.4%), Prunus serotina Ehrhart var. serotina (black cherry) (0.25%), Picea glauca (Moench) Voss (white spruce) (0.3%) were observed now. Compared with historical records (Keddy K. , 1993) showing hemlock at 27.62%. MNR FRI estimates black cherry and white spruce at 2.2% each (MNRF, 2009). Some recommendations coming out of this work include reintroducing species that were originally present but are missing now, as well as recommendations on how to prepare the forest for climate change and defend against external threats faced by pests and diseases that may have detrimental effect on existing tree species. Some examples of such threats include the Emerald Ash Borer (EAB), Asian Long Horned Beetle (ALNB), Beach Bark Disease (BBD) and Oak Wilt (OW). Climate change may challenge certain species that may need replacement with trees currently growing in slightly warmer regions of southern Ontario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".